Wykorzystanie głębokich sieci neuronowych w ograniczaniu zmian klimatycznych związanych z konfliktem farmerów i pasterzy w ramach inicjatywy na rzecz zrównoważonej integracji społecznej

QAZI et al., 2015, The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review, in: Journal of Cleaner Production, 104.
  Google Scholar

NDEHEDEHE C.E., AGUTU N.O., OKWUASHI O., FERREIRAD V.G., 2016, Spatio-temporal variability of droughts and terrestrial water storage over Lake Chad Basin using independent component analysis, in: Journal of Hydrology, 540, p. 106-128.
  Google Scholar

BUMA W., LEE S., 2016, Investigating the Changes within the Lake Chad Basin Using GRACE and LANDSAT Imageries, in: Procedia Engineering, 154, p. 403-405.
  Google Scholar

JEONG D.H., LEE J.M., 2018, Enhancement of modifier adaptation scheme via feedforward decision maker using historical disturbance data and deep machine learning, in: Computers & Chemical Engineering, 108, p. 31-46.
  Google Scholar

HAMID A.M., BABA I.M., 2014, Resolving Nigeria's ‘Boko Haram’ Insurgence: What Role for the Media?, in: Procedia – Social and Behavioral Sciences, 155, p. 14-20.
  Google Scholar

TUBI A., FEITELSON E., 2016, Drought and cooperation in a conflict prone area: Bedouin herders and Jewish farmers in Israel's northern Negev 1957-1963, in: Political Geography, 51, p. 30-42.
  Google Scholar

GWANGWAZO K.M., 2018, Only justice, fairness will stop Fulani herdsmen, farmers clashes, in: The Vanguard, 19 January.
  Google Scholar

GOAL 13: CLIMATE ACTION, 2015, in: Sustainable Development Goals (SDGs), United Nations Development Programme.
  Google Scholar

KUMARA S., PANDEY A., SATWIK K.S.R., KUMAR S., SINGH S.K., SINGH A.K., MOHAN A., 2018, Deep learning framework for recognition of cattle using muzzle point image pattern, in: Measurement, 116, p. 1-17.
  Google Scholar

NWABUGHIOGU L., 2017, Ecological Funds: NEC approves N2bn each for APC states, in: Vanguard Newspaper, May 26.
  Google Scholar

GALINATOA G.I. AND GALINATO S.P., 2013, The short-run and long-run effects of corruption control and political stability on forest cover, in: Ecological Economics, 89, p. 153-161.
  Google Scholar

ABUBAKAR I., 2017, Understanding Military Operations as Safeguard of State, in: The Post, November 28.
  Google Scholar

NWACHUKWU O., 2018, Umahi heads committee to dialogue with Miyetti Allah, others over Fulani herdsmen/farmers clash, in: Bussiness Day Newspaper, February 2.
  Google Scholar

MATEMILOLA S., ADEDEJI O.H., ENOGUANBHOR E.C., 2018, Land Use/Land Cover Change in Petroleum-Producing Regions of Nigeria, in: The Political Ecology of Oil and Gas Activities in the Nigerian Aquatic Ecosystem, p. 257-276.
  Google Scholar

UGWUANYI S., 2018, Benue youths reveal what they fear about Exercise Ayem Akpatuma, in: Daily Post, February 21.
  Google Scholar

GOAL 16: PEACE, JUSTICE, AND STRONG INSTITUTIONS, 2015, in: Sustainable Development Goals (SDGs), United Nations Development Programme.
  Google Scholar

SHENA Y., ZHOUA H., LIA J., JIANB F., JAYASB D.S., 2018, Detection of stored-grain insects using deep learning, in: Computers and Electronics in Agriculture, 145, p. 319-325.
  Google Scholar

ALI L., HATALA M., GAŠEVIC D. JOVANOVIC J., 2012, A qualitative evaluation of evolution of a learning analytics tool, in: Computers & Education 58(1), p. 470-489.
  Google Scholar

KAMILARIS A., PRENAFETA-BOLDÚ F.X., 2018, Deep learning in agriculture: A survey, in: Computers and Electronics in Agriculture, 147, p. 70-90.
  Google Scholar

LU Y., YI S., ZENG N., LIU Y., ZHANG Y., 2017, Identification of rice diseases using deep convolutional neural networks, in: Neurocomputing, 267, p. 378-384.
  Google Scholar

SONAWANE S., CHOUBEY N.S., 2017, A Systematic Literature Review on Soybean Quality Assessment and Utility of Neural Network in Seed Classification, in: International Journal of Current Research, 9(05), p. 51160-51165.
  Google Scholar

UKAMAKA M., EDWARD D., SALIFU D., IGBOKWE E.M., 2016, Resource use conflict in agrarian communities, management and challenges: A case of farmer-herdsmen conflict in Kogi State, Nigeria, in: Journal of Rural Studies, 46, p. 147-154.
  Google Scholar

REUVENY R., 2007, Climate change-induced migration and violent conflict, in: Political Geography, 26(6), p. 656-673.
  Google Scholar

NEWS AGENCY OF NIGERIA, 2018, Umahi orders traditional rulers to compile data on herdsmen, in: Vanguard Newspaper, February 9.
  Google Scholar

DIMILILERA K., KIANIB E., 2017, Application of back propagation neural networks on maize plant detection, in: Procedia Computer Science, 120, p, 376-381.
  Google Scholar

BOUKIS C., MANDIC D.P., CONSTANTINIDES A.G., 2009, A class of stochastic gradient algorithms with exponentiated error cost functions, in: Digital Signal Processing, 19(2), p. 201-212.
  Google Scholar

AGBESE D., 2017, Fulani herdsmen? Here are the grim statistics, in: The Guardian, 03 November.
  Google Scholar

OKEWU E., MASKELIUNAS R., MISRA S.,DAMASEVICIUS R., FERNADEZ-SANZ L., 2018, An e-Environment System for Socio-economic Sustainability and National Security, in: Problemy Ekorozwoju/ Problems of Sutainable Development, 13(1), p. 121-132.
  Google Scholar

OKEWU E., MASKELIUNAS R., MISRA S., DAMASEVICIUS R., FERNADEZ-SANZ L., 2018, Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem, in: Journal of Sustainability 2017(9).
  Google Scholar

OKEWU E., MISRA S., OKEWU J., 2017, Model-Driven Engineering and Creative Arts Approach to Designing Climate Change Response System for Rural Africa: A Case Study of Adum-Aiona Community in Nigeria, in: Problemy Ekorozwoju/ Problems of Sustainable Development, 12(1), p. 101-116.
  Google Scholar

NATIONAL BUREAU OF STATISTICS, 2011, Annual Abstract of Statistics 2011, Federal Republic of Nigeria, http://www.nigerianstat.gov.ng (1.12.2018).
  Google Scholar

OLUBUSOYE O.E, KORTER G.O., KESHINRO O.A., 2015, Nigerian Statistical System: The Evolution, Progress And Challenges, Research, https://www.doi.org/10.13140/RG.2.1.3136.4569, https://www.Researchgate.Net/Publication/283715250 (1.12.2018).
  Google Scholar